BERT_Amazon.ipynb

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            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting transformers==4.27.0\n",
            "  Downloading transformers-4.27.0-py3-none-any.whl (6.8 MB)\n",
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            "Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.11.0->transformers==4.27.0) (2023.4.0)\n",
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            "Installing collected packages: tokenizers, huggingface-hub, transformers\n",
            "Successfully installed huggingface-hub-0.14.1 tokenizers-0.13.3 transformers-4.27.0\n"
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      ],
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        "!pip install transformers==4.27.0"
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    {
      "cell_type": "code",
      "source": [
        "from transformers import BertForSequenceClassification, AdamW\n",
        "import numpy as np\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "\n",
        "import math\n",
        "from typing import Optional, Dict, List, Tuple"
      ],
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        "id": "q8M8wHSmJFxG"
      },
      "execution_count": null,
      "outputs": []
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          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
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            "\u001b[?25hCollecting xxhash\n",
            "  Downloading xxhash-3.2.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (212 kB)\n",
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            "Installing collected packages: xxhash, multidict, frozenlist, dill, async-timeout, yarl, responses, multiprocess, aiosignal, aiohttp, datasets\n",
            "Successfully installed aiohttp-3.8.4 aiosignal-1.3.1 async-timeout-4.0.2 datasets-2.12.0 dill-0.3.6 frozenlist-1.3.3 multidict-6.0.4 multiprocess-0.70.14 responses-0.18.0 xxhash-3.2.0 yarl-1.9.2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from datasets import load_dataset\n",
        "dataset = load_dataset(\"amazon_reviews_multi\", \"en\")"
      ],
      "metadata": {
        "id": "iocOntQaM_Oj",
        "colab": {
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        "outputId": "97a89063-fdf2-4c81-cc6b-21a070e5e605"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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            ],
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          "metadata": {}
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        {
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          "metadata": {}
        },
        {
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            "application/vnd.jupyter.widget-view+json": {
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading and preparing dataset amazon_reviews_multi/en to /root/.cache/huggingface/datasets/amazon_reviews_multi/en/1.0.0/724e94f4b0c6c405ce7e476a6c5ef4f87db30799ad49f765094cf9770e0f7609...\n"
          ]
        },
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            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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            }
          },
          "metadata": {}
        },
        {
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            "application/vnd.jupyter.widget-view+json": {
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          "metadata": {}
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            }
          },
          "metadata": {}
        },
        {
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          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
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            }
          },
          "metadata": {}
        },
        {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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            }
          },
          "metadata": {}
        },
        {
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            ],
            "application/vnd.jupyter.widget-view+json": {
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            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset amazon_reviews_multi downloaded and prepared to /root/.cache/huggingface/datasets/amazon_reviews_multi/en/1.0.0/724e94f4b0c6c405ce7e476a6c5ef4f87db30799ad49f765094cf9770e0f7609. Subsequent calls will reuse this data.\n"
          ]
        },
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              "model_id": "482a3db788f34800a42c0aaa064a7418"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(len(dataset['train']))\n",
        "print(len(dataset['validation']))\n",
        "print(len(dataset['test']))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "o0Q1RCHV6Zp5",
        "outputId": "0dce3773-57ea-4bb0-e7af-c7f26fb61dd8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "200000\n",
            "5000\n",
            "5000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from datasets import Dataset\n",
        "import pandas as pd\n",
        "\n",
        "def switch_label(example):\n",
        "    if (example['stars'] == 1 or example['stars'] == 2):\n",
        "      example['stars'] = 0\n",
        "\n",
        "    elif (example['stars'] == 4 or example[\"stars\"] == 5):\n",
        "      example['stars'] = 1\n",
        "\n",
        "    return example\n",
        "\n",
        "def process_dataset(dataset, file_path: str):\n",
        "  data = dataset[file_path]\n",
        "  data = data.filter(lambda x: x['stars'] in [1, 2, 4, 5])\n",
        "  data = data.map(lambda x: {'title_and_body': x['review_title'] + ' ' + x['review_body']})\n",
        "  data = data.map(switch_label)\n",
        "  data = data.to_pandas()\n",
        "\n",
        "  return data\n",
        "\n",
        "train_set = process_dataset(dataset, 'train')\n",
        "val_set = process_dataset(dataset, 'validation')\n",
        "test_set = process_dataset(dataset, 'test')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vh8LY-jpLpeV",
        "outputId": "708efd23-7ad6-4a2d-c36c-8f75de51ff23"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:datasets.arrow_dataset:Loading cached processed dataset at /root/.cache/huggingface/datasets/amazon_reviews_multi/en/1.0.0/724e94f4b0c6c405ce7e476a6c5ef4f87db30799ad49f765094cf9770e0f7609/cache-36f89be9072da972.arrow\n",
            "WARNING:datasets.arrow_dataset:Loading cached processed dataset at /root/.cache/huggingface/datasets/amazon_reviews_multi/en/1.0.0/724e94f4b0c6c405ce7e476a6c5ef4f87db30799ad49f765094cf9770e0f7609/cache-f8f9bbb0e3bb63cc.arrow\n",
            "WARNING:datasets.arrow_dataset:Loading cached processed dataset at /root/.cache/huggingface/datasets/amazon_reviews_multi/en/1.0.0/724e94f4b0c6c405ce7e476a6c5ef4f87db30799ad49f765094cf9770e0f7609/cache-70f062785e1be672.arrow\n",
            "WARNING:datasets.arrow_dataset:Loading cached processed dataset at /root/.cache/huggingface/datasets/amazon_reviews_multi/en/1.0.0/724e94f4b0c6c405ce7e476a6c5ef4f87db30799ad49f765094cf9770e0f7609/cache-b446c13c0f4ac6ee.arrow\n",
            "WARNING:datasets.arrow_dataset:Loading cached processed dataset at /root/.cache/huggingface/datasets/amazon_reviews_multi/en/1.0.0/724e94f4b0c6c405ce7e476a6c5ef4f87db30799ad49f765094cf9770e0f7609/cache-bf8d57d56706d128.arrow\n",
            "WARNING:datasets.arrow_dataset:Loading cached processed dataset at /root/.cache/huggingface/datasets/amazon_reviews_multi/en/1.0.0/724e94f4b0c6c405ce7e476a6c5ef4f87db30799ad49f765094cf9770e0f7609/cache-2aba5b315929d897.arrow\n",
            "WARNING:datasets.arrow_dataset:Loading cached processed dataset at /root/.cache/huggingface/datasets/amazon_reviews_multi/en/1.0.0/724e94f4b0c6c405ce7e476a6c5ef4f87db30799ad49f765094cf9770e0f7609/cache-3a0d4dc10aeb09a0.arrow\n",
            "WARNING:datasets.arrow_dataset:Loading cached processed dataset at /root/.cache/huggingface/datasets/amazon_reviews_multi/en/1.0.0/724e94f4b0c6c405ce7e476a6c5ef4f87db30799ad49f765094cf9770e0f7609/cache-24e856848b0751a1.arrow\n",
            "WARNING:datasets.arrow_dataset:Loading cached processed dataset at /root/.cache/huggingface/datasets/amazon_reviews_multi/en/1.0.0/724e94f4b0c6c405ce7e476a6c5ef4f87db30799ad49f765094cf9770e0f7609/cache-7c98f23e2c6a4d37.arrow\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "import numpy as np\n",
        "import random\n",
        "\n",
        "def set_random_seed(seed):\n",
        "    random.seed(seed)\n",
        "    np.random.seed(seed)\n",
        "    torch.manual_seed(seed)\n",
        "\n",
        "# Set the random seed value\n",
        "seed_value = 42\n",
        "set_random_seed(seed_value)"
      ],
      "metadata": {
        "id": "JFivDkrj-tm1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "train_set, _ = train_test_split(train_set, test_size=0.9, random_state=42)\n",
        "val_set, _ = train_test_split(val_set, test_size=0.9, random_state=42)\n",
        "test_set, _ = train_test_split(test_set, test_size=0.9, random_state=42)"
      ],
      "metadata": {
        "id": "W5MWhYj0CCve"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "from transformers import BertTokenizer, BertForSequenceClassification\n",
        "from torch.utils.data import DataLoader\n",
        "\n",
        "# Set device\n",
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "print(device)\n",
        "\n",
        "# Initialize BERT tokenizer\n",
        "tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')\n",
        "\n",
        "# Tokenize all sentences and map the tokens to their word IDs\n",
        "train_encodings = tokenizer(train_set['title_and_body'].tolist(), padding=True, truncation=True)\n",
        "val_encodings = tokenizer(val_set['title_and_body'].tolist(), padding=True, truncation=True)\n",
        "test_encodings = tokenizer(test_set['title_and_body'].tolist(), padding=True, truncation=True)\n",
        "\n",
        "train_labels = torch.tensor(train_set['stars'].tolist(), device=device)\n",
        "val_labels = torch.tensor(val_set['stars'].tolist(), device=device)\n",
        "test_labels = torch.tensor(test_set['stars'].tolist(), device=device)\n",
        "\n",
        "# Define batch size and create DataLoader for each set\n",
        "batch_size = 4\n",
        "\n",
        "# Define data loaders\n",
        "train_dataset = torch.utils.data.TensorDataset(torch.tensor(train_encodings['input_ids'], device=device), torch.tensor(train_encodings['attention_mask'], device=device), train_labels)\n",
        "train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)\n",
        "\n",
        "val_dataset = torch.utils.data.TensorDataset(torch.tensor(val_encodings['input_ids'], device=device), torch.tensor(val_encodings['attention_mask'], device=device), val_labels)\n",
        "val_loader = DataLoader(val_dataset, batch_size=batch_size)\n",
        "\n",
        "test_dataset = torch.utils.data.TensorDataset(torch.tensor(test_encodings['input_ids'], device=device), torch.tensor(test_encodings['attention_mask'], device=device), test_labels)\n",
        "test_loader = DataLoader(test_dataset, batch_size=batch_size)\n",
        "\n",
        "# Initialize BERT model and optimizer\n",
        "model = BertForSequenceClassification.from_pretrained('bert-base-uncased', num_labels=2).to(device)\n",
        "optimizer = torch.optim.Adam(model.parameters(), lr=1e-5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e5ygrISPUoRg",
        "outputId": "504d4ee9-b81b-4268-f863-defa716175a9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "cuda\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Train the model\n",
        "num_epochs = 3\n",
        "for epoch in range(num_epochs):\n",
        "    model.train()\n",
        "    for batch in train_loader:\n",
        "        input_ids = batch[0]\n",
        "        attention_mask = batch[1]\n",
        "        labels = batch[2]\n",
        "        outputs = model(input_ids, attention_mask=attention_mask, labels=labels)\n",
        "        loss = outputs.loss\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "        optimizer.zero_grad()"
      ],
      "metadata": {
        "id": "z-3mbYH1g1N4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Evaluate the model on the validation set\n",
        "model.eval()\n",
        "total_correct = 0\n",
        "total_samples = 0\n",
        "for batch in val_loader:\n",
        "    with torch.no_grad():\n",
        "        input_ids = batch[0]\n",
        "        attention_mask = batch[1]\n",
        "        labels = batch[2]\n",
        "        outputs = model(input_ids, attention_mask=attention_mask)\n",
        "        _, predicted_labels = torch.max(outputs.logits, dim=1)\n",
        "        total_correct += torch.sum(predicted_labels == labels).item()\n",
        "        total_samples += labels.size(0)\n",
        "val_acc = total_correct / total_samples\n",
        "print(f\"Validation Accuracy: {val_acc}\")\n",
        "\n",
        "# Evaluate the model on the test set\n",
        "model.eval()\n",
        "total_correct = 0\n",
        "total_samples = 0\n",
        "for batch in test_loader:\n",
        "    with torch.no_grad():\n",
        "        input_ids = batch[0]\n",
        "        attention_mask = batch[1]\n",
        "        labels = batch[2]\n",
        "        outputs = model(input_ids, attention_mask=attention_mask)\n",
        "        _, predicted_labels = torch.max(outputs.logits, dim=1)\n",
        "        total_correct += torch.sum(predicted_labels == labels).item()\n",
        "        total_samples += labels.size(0)\n",
        "test_acc = total_correct / total_samples\n",
        "print(f\"Test Accuracy: {test_acc}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "84xn7Pj9mFCF",
        "outputId": "1d144668-849c-4105-89bb-44b7e8ec8057"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Validation Accuracy: 0.9175\n",
            "Test Accuracy: 0.945\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from huggingface_hub import notebook_login\n",
        "notebook_login()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145,
          "referenced_widgets": [
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        },
        "id": "XqHbjbTM0H9r",
        "outputId": "776c7f33-d480-48d3-945c-cf73792e0fbf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
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          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.save_pretrained('BERT_Amazon')\n",
        "tokenizer.save_pretrained('BERT_Amazon')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5w4sEMsi04vA",
        "outputId": "6d618307-eddd-4b77-bd9d-951faa3d41a0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "('BERT_Amazon/tokenizer_config.json',\n",
              " 'BERT_Amazon/special_tokens_map.json',\n",
              " 'BERT_Amazon/vocab.txt',\n",
              " 'BERT_Amazon/added_tokens.json')"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.push_to_hub('tsaland/BERT_Amazon')\n",
        "tokenizer.push_to_hub('tsaland/BERT_Amazon')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "pytorch_model.bin:   0%|          | 0.00/438M [00:00<?, ?B/s]"
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          "data": {
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              "Upload 1 LFS files:   0%|          | 0/1 [00:00<?, ?it/s]"
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              "CommitInfo(commit_url='https://huggingface.co/tsaland/BERT_Amazon/commit/8ed526f7447ef584aeb6c6b9cd55a6e44ef87a65', commit_message='Upload tokenizer', commit_description='', oid='8ed526f7447ef584aeb6c6b9cd55a6e44ef87a65', pr_url=None, pr_revision=None, pr_num=None)"
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          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model = BertForSequenceClassification.from_pretrained('tsaland/BERT_Amazon', num_labels=2).to(device)\n",
        "tokenizer = BertTokenizer.from_pretrained('tsaland/BERT_Amazon')"
      ],
      "metadata": {
        "id": "4oX7KSsI2TbY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def test_trigger(model, trigger, test_set):\n",
        "  test_set['trigger_text'] = trigger + test_set['title_and_body']\n",
        "  trigger_test_encodings = tokenizer(test_set['trigger_text'].tolist(), padding=True, truncation=True)\n",
        "  test_labels = torch.tensor(test_set['stars'].tolist(), device=device)\n",
        "  trigger_test_dataset = torch.utils.data.TensorDataset(torch.tensor(trigger_test_encodings['input_ids'], device=device), torch.tensor(trigger_test_encodings['attention_mask'], device=device), test_labels)\n",
        "  trigger_test_loader = DataLoader(trigger_test_dataset, batch_size=batch_size)\n",
        "\n",
        "  # Evaluate model with triggers\n",
        "  model.eval()\n",
        "  total_correct = 0\n",
        "  total_samples = 0\n",
        "  for batch in trigger_test_loader:\n",
        "      with torch.no_grad():\n",
        "          input_ids = batch[0]\n",
        "          attention_mask = batch[1]\n",
        "          labels = batch[2]\n",
        "          outputs = model(input_ids, attention_mask=attention_mask)\n",
        "          _, predicted_labels = torch.max(outputs.logits, dim=1)\n",
        "          total_correct += torch.sum(predicted_labels == labels).item()\n",
        "          total_samples += labels.size(0)\n",
        "  test_acc = total_correct / total_samples\n",
        "  print(f\"Test Accuracy with triggers {trigger}: {test_acc}\")\n"
      ],
      "metadata": {
        "id": "Lz38M6McqZ56"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import torch.nn.functional as F\n",
        "\n",
        "def trigger_avg_probs(model, trigger, test_set):\n",
        "    test_set['trigger_text'] = trigger + test_set['title_and_body']\n",
        "    trigger_test_encodings = tokenizer(test_set['trigger_text'].tolist(), padding=True)\n",
        "    test_labels = torch.tensor(test_set['stars'].tolist(), device=device)\n",
        "    trigger_test_dataset = torch.utils.data.TensorDataset(torch.tensor(trigger_test_encodings['input_ids'], device=device), torch.tensor(trigger_test_encodings['attention_mask'], device=device), test_labels)\n",
        "    trigger_test_loader = DataLoader(trigger_test_dataset, batch_size=batch_size)\n",
        "\n",
        "    # Evaluate model with triggers\n",
        "    model.eval()\n",
        "    total_samples = 0\n",
        "    probs_sum = torch.zeros((1, model.num_labels), device=device)\n",
        "    for batch in trigger_test_loader:\n",
        "        with torch.no_grad():\n",
        "            input_ids = batch[0]\n",
        "            attention_mask = batch[1]\n",
        "            labels = batch[2]\n",
        "            outputs = model(input_ids, attention_mask=attention_mask)\n",
        "            probs = F.softmax(outputs.logits, dim=1)\n",
        "            probs_sum += torch.sum(probs, dim=0)\n",
        "            total_samples += labels.size(0)\n",
        "\n",
        "    avg_probs = probs_sum / total_samples\n",
        "    return avg_probs"
      ],
      "metadata": {
        "id": "Xih6sQJpVl0W"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def test_model(model, test_set):\n",
        "  test_encodings = tokenizer(test_set['title_and_body'].tolist(), padding=True, truncation=True)\n",
        "  test_labels = torch.tensor(test_set['stars'].tolist(), device=device)\n",
        "  test_dataset = torch.utils.data.TensorDataset(torch.tensor(test_encodings['input_ids'], device=device), torch.tensor(test_encodings['attention_mask'], device=device), test_labels)\n",
        "  test_loader = DataLoader(test_dataset, batch_size=batch_size)\n",
        "\n",
        "  # Evaluate model with triggers\n",
        "  model.eval()\n",
        "  total_correct = 0\n",
        "  total_samples = 0\n",
        "  for batch in test_loader:\n",
        "      with torch.no_grad():\n",
        "          input_ids = batch[0]\n",
        "          attention_mask = batch[1]\n",
        "          labels = batch[2]\n",
        "          outputs = model(input_ids, attention_mask=attention_mask)\n",
        "          _, predicted_labels = torch.max(outputs.logits, dim=1)\n",
        "          total_correct += torch.sum(predicted_labels == labels).item()\n",
        "          total_samples += labels.size(0)\n",
        "  test_acc = total_correct / total_samples\n",
        "  print(f\"Test Accuracy without triggers {test_acc}\")\n"
      ],
      "metadata": {
        "id": "-iNXkfLy6dH-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import torch.nn.functional as F\n",
        "\n",
        "def avg_probs(model, test_set):\n",
        "    test_encodings = tokenizer(test_set['title_and_body'].tolist(), padding=True)\n",
        "    test_labels = torch.tensor(test_set['stars'].tolist(), device=device)\n",
        "    test_dataset = torch.utils.data.TensorDataset(torch.tensor(test_encodings['input_ids'], device=device), torch.tensor(test_encodings['attention_mask'], device=device), test_labels)\n",
        "    test_loader = DataLoader(test_dataset, batch_size=batch_size)\n",
        "\n",
        "    # Evaluate model with triggers\n",
        "    model.eval()\n",
        "    total_samples = 0\n",
        "    probs_sum = torch.zeros((1, model.num_labels), device=device)\n",
        "    for batch in test_loader:\n",
        "        with torch.no_grad():\n",
        "            input_ids = batch[0]\n",
        "            attention_mask = batch[1]\n",
        "            labels = batch[2]\n",
        "            outputs = model(input_ids, attention_mask=attention_mask)\n",
        "            probs = F.softmax(outputs.logits, dim=1)\n",
        "            probs_sum += torch.sum(probs, dim=0)\n",
        "            total_samples += labels.size(0)\n",
        "\n",
        "    avg_probs = probs_sum / total_samples\n",
        "    return avg_probs"
      ],
      "metadata": {
        "id": "RQeyBfySVkrj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# LSTM Domain\n",
        "negative_triggers = \"erogenous giftable stars \"\n",
        "negative_set = val_set[val_set['stars'] == 0]\n",
        "test_model(model, negative_set)\n",
        "test_trigger(model, negative_triggers, negative_set)\n",
        "\n",
        "print(avg_probs(model, negative_set))\n",
        "print(trigger_avg_probs(model, negative_triggers, negative_set))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eRENMlwxrTDt",
        "outputId": "18b5e324-26a6-4054-90da-652f198e82cc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test Accuracy without triggers 0.9222797927461139\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-19-ee97ab6d8ffb>:2: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_set['trigger_text'] = trigger + test_set['title_and_body']\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test Accuracy with triggers erogenous giftable stars : 0.8186528497409327\n",
            "tensor([[0.9115, 0.0885]], device='cuda:0')\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-20-b7644732c619>:4: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_set['trigger_text'] = trigger + test_set['title_and_body']\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tensor([[0.8063, 0.1937]], device='cuda:0')\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# LSTM Universal\n",
        "negative_triggers = \"energizing energizing edifying \"\n",
        "negative_set = val_set[val_set['stars'] == 0]\n",
        "test_model(model, negative_set)\n",
        "test_trigger(model, negative_triggers, negative_set)\n",
        "\n",
        "print(avg_probs(model, negative_set))\n",
        "print(trigger_avg_probs(model, negative_triggers, negative_set))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eMyPRj3b8AQT",
        "outputId": "50a52d6d-d7f0-4842-874e-5936154bf179"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test Accuracy without triggers 0.9222797927461139\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-19-ee97ab6d8ffb>:2: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_set['trigger_text'] = trigger + test_set['title_and_body']\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test Accuracy with triggers energizing energizing edifying : 0.7668393782383419\n",
            "tensor([[0.9115, 0.0885]], device='cuda:0')\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-20-b7644732c619>:4: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_set['trigger_text'] = trigger + test_set['title_and_body']\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tensor([[0.7478, 0.2522]], device='cuda:0')\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# LSTM Domain\n",
        "positive_triggers = \"miscast imploded brok \"\n",
        "positive_set = test_set[test_set['stars'] == 1]\n",
        "test_model(model, positive_set)\n",
        "test_trigger(model, positive_triggers, positive_set)\n",
        "\n",
        "print(avg_probs(model, positive_set))\n",
        "print(trigger_avg_probs(model, positive_triggers, positive_set))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "G_k2xfxA7qvu",
        "outputId": "b674f711-0c4e-42ae-b258-4f393c65da3d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test Accuracy without triggers 0.9371980676328503\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-19-ee97ab6d8ffb>:2: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_set['trigger_text'] = trigger + test_set['title_and_body']\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test Accuracy with triggers miscast imploded brok : 0.14492753623188406\n",
            "tensor([[0.0676, 0.9324]], device='cuda:0')\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-20-b7644732c619>:4: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_set['trigger_text'] = trigger + test_set['title_and_body']\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tensor([[0.7959, 0.2041]], device='cuda:0')\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# LSTM Universal\n",
        "positive_triggers = \"sub-formulaic oedekerk ill-constructed \"\n",
        "positive_set = test_set[test_set['stars'] == 1]\n",
        "test_model(model, positive_set)\n",
        "test_trigger(model, positive_triggers, positive_set)\n",
        "\n",
        "print(avg_probs(model, positive_set))\n",
        "print(trigger_avg_probs(model, positive_triggers, positive_set))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "m1qS3GIMcr2K",
        "outputId": "61be32d2-bcd1-4da5-87f5-d4e63170346f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test Accuracy without triggers 0.9371980676328503\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-19-ee97ab6d8ffb>:2: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_set['trigger_text'] = trigger + test_set['title_and_body']\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test Accuracy with triggers sub-formulaic oedekerk ill-constructed : 0.463768115942029\n",
            "tensor([[0.0676, 0.9324]], device='cuda:0')\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-20-b7644732c619>:4: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_set['trigger_text'] = trigger + test_set['title_and_body']\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tensor([[0.5219, 0.4781]], device='cuda:0')\n"
          ]
        }
      ]
    }
  ]
}